A new tool wear monitoring method based on multi-scale PCA

A multi-scale principal component analysis (MSPCA) method is presented to realize online tool wear monitoring of milling process. In this method, the training sample set of normal operational condition is decomposed into different scales using wavelet multi resolution analysis. The statistical indices and the corresponding control limits are constructed to monitor the tool wear based on principal component analysis (PCA). By integration of PCA with wavelet transformation, the accuracy and robustness of tool wear monitoring model can be improved greatly. To test the effectiveness of the proposed method, a Ti–6Al–4V milling experiment was carried out. Force and vibration signals during the machining process were collected simultaneously to depict the characteristics of the tool wear variation. Based on the extracted root mean square and kurtosis features, the tool wear monitoring is realized by MSPCA and PCA respectively. The analysis and comparison results show that MSPCA can produce higher accuracy in comparison with PCA.